Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

Abstractive dialogue summarization is the task of capturing highlights a and rewriting them into concise version. In this paper, we present novel multi-speaker summarizer to demonstrate how large-scale commonsense knowledge can facilitate understanding summary generation. detail, consider utterance as two different types data design Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile, also add speakers heterogeneous nodes information flow. Experimental results on SAMSum dataset show that our model outperform various methods. We conduct zero-shot setting experiments Argumentative Summary Corpus, better generalized new domain.